Can mood primitives predict apparent personality?

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Abstract

First impressions play a critical role in shaping social interactions and consequently have a high impact on people’s lives. This study presents an explainable system that models apparent personality traits that influence first impressions as a function of automatically predicted arousal, valence and likeability (AVL) scores. To this end, we enrich the ChaLearn Looking at People - First Impressions (LAP-FI) dataset by annotating a portion of it for the AVL dimensions and carry out extensive uni-modal and multimodal experiments by using state-of-the-art acoustic, visual and linguistic features. We propose to use a glass-box model, namely, Explainable Boosting Machine, to model the Big Five personality traits. Our results demonstrate that personality trait impressions can be effectively predicted through the mood and likeability scores of a given video. We show that the proposed model, which is trained on only a few features, not only provides more meaningful explanations but also yields competitive performance (with a 0.09 Mean Absolute Error) compared to the state-of-the-art methods. The annotated benchmark dataset and the scripts to reproduce the results are available at: https://github.com/gizemsogancioglu/mood-project.
Original languageEnglish
Title of host publication2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Print)978-1-6654-0020-6
DOIs
Publication statusPublished - 1 Oct 2021
Event2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII) - Nara, Japan
Duration: 28 Sept 20211 Oct 2021

Conference

Conference2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)
Period28/09/211/10/21

Keywords

  • Big Five personality traits
  • Affective computing
  • Mood recognition
  • Predictive models
  • Linguistics
  • Benchmark testing
  • Multimodal fusion
  • Arousal
  • Valence

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